An optimal control perspective on diffusion-based generative modeling
- URL: http://arxiv.org/abs/2211.01364v3
- Date: Tue, 26 Mar 2024 17:45:01 GMT
- Title: An optimal control perspective on diffusion-based generative modeling
- Authors: Julius Berner, Lorenz Richter, Karen Ullrich,
- Abstract summary: We establish a connection between optimal control and generative models based on differential equations (SDEs)
In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals.
We develop a novel diffusion-based method for sampling from unnormalized densities.
- Score: 9.806130366152194
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs), such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to transfer methods from optimal control theory to generative modeling. First, we show that the evidence lower bound is a direct consequence of the well-known verification theorem from control theory. Further, we can formulate diffusion-based generative modeling as a minimization of the Kullback-Leibler divergence between suitable measures in path space. Finally, we develop a novel diffusion-based method for sampling from unnormalized densities -- a problem frequently occurring in statistics and computational sciences. We demonstrate that our time-reversed diffusion sampler (DIS) can outperform other diffusion-based sampling approaches on multiple numerical examples.
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